Existing seismic exploration methods primarily focus on the properties of the sound-reflecting boundaries present in the earth's interior. These methods are founded on theoretical conclusions and experimental observations that the strength of the sound reflection from the boundary itself is determined by certain lithological properties of rock within the layer above and the layer below this boundary.
However, reflection-boundary based methods are indirect at best. Reflections at each point on a boundary depend on no less than seven variables (P-wave velocity above, S wave velocity above, density above, P wave velocity below, S wave velocity below, density below, and angle). The interplay between these variables makes it difficult to determine any particular one with accuracy. Even under theoretically ideal measurement conditions, boundary-based theories sometimes fail. For example, the acoustic impedance contrast between sand and shale disappears in a wide age/compaction range, thus preventing or nearly preventing any boundary reflection at all.
When employing reflection-boundary based methods, it often becomes necessary to rely on additional “outside” information to interpret the seismic data. The additional data may come in the form of hypothesized models of the subsurface structure and data (“logs”) from existing wells. (Typically, data from well logs is extrapolated away from the well bore along reflecting boundaries.) However, combining such forms of outside data with seismic data requires the use of additional assumptions that may or may not be valid. In the case of extrapolated well log data, there is no way to tell when the quality of the extrapolation has degraded to the point where more harm than good is being done.
Efforts to refine reflection-boundary based methods continue. Various existing or proposed methods employ neural networks (with “supervised” or “unsupervised” training) that combine large numbers of attributes to construct a reservoir model. Still other methods are inversion-based, combining well data, geophysical data, geologic data, reservoir engineering data, and geo-statistical data to construct a reservoir model. These methods have proven to be extremely complex (and expensive), involving many professionals from different disciplines in a chain that can be limited by its weakest link.
Accordingly, it would be desirable to have a reliable method of hydrocarbon detection that does not rely on reflection-boundary analysis, outside information, or unduly complex models. U.S. Pat. No. 5,414,674 issued to Lichman (“the Lichman patent”) discloses a method based on resonant energy analysis of seismic data. This patent is incorporated by reference in its entirety.
The method disclosed in the Lichman patent analyzes the resonant responses generated when a seismic wave passes through a given stratum. The seismic responses are mapped onto the quefrency domain in order to separate the resonant and non-resonant components of the reflected energy. Strata that consist of predominantly elastic materials (solids and liquids) resonate in discrete frequency bands, which are represented by a quefrency spectrum with a large amplitude at high-quefrency values. Gas-bearing strata have more plastic properties and emit a more uniform response that lacks distinct resonant peaks. The Quefrency spectrum of gas-bearing strata contains a relatively higher amplitude at low-quefrency values. Therefore, strata having high concentration of natural gas hydrocarbons are located by detecting seismic data having quefrency distributions weighted toward the lower quefrencies.
The Lichman patent shows that methods not based on reflection-boundary analysis can be useful in locating gas reservoirs. There still exists a need for better methods that can be used to locate hydrocarbon (both liquid and gas) reservoirs.